Jean Honorio

Assistant Professor in the Computer Science Department at Purdue.
Assistant Professor in the Statistics Department (by courtesy) at Purdue.
Lawson Building 2142-J, West Lafayette, IN 47907, phone: 765-496-6757
e-mail: jhonorio at purdue.edu

Through a unifying framework, with the power of continuous relaxations and primal-dual certificates, my research group produces novel algorithms for learning and inference in combinatorial problems. Our aim is to generate correct, computationally efficient and statistically efficient algorithms for high dimensional machine learning problems. Our results include algorithms for learning and inference in structured prediction, community detection, learning Bayesian networks and graphical games. [vita]

Prior to joining Purdue, I was a postdoctoral associate at MIT CSAIL, working with Tommi Jaakkola. My Erdős number is 3: Jean Honorio → Tommi Jaakkola → Noga Alon → Paul Erdős.

Students

All Publications (by year / by topic)

1. Structured Prediction

On the Fundamental Limits of Exact Inference in Structured Prediction. (Preprint)
Lee H., Bello K., Honorio J.
(Under submission.)

A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy. (Preprint)
Bello K., C. Ke, Honorio J.
(Under submission.)

Exact Inference with Latent Variables in an Arbitrary Domain. (Preprint)
Ke C., Honorio J.
(Under submission.)

Minimax Bounds for Structured Prediction Based on Factor Graphs.
Bello K., Ghoshal A., Honorio J.
Artificial Intelligence and Statistics. Palermo/Italy, 2020.

Exact Inference in Structured Prediction.
Bello K., Honorio J.
Neural Information Processing Systems. Vancouver/Canada, 2019.

Learning Latent Variable Structured Prediction Models with Gaussian Perturbations.
Bello K., Honorio J.
Neural Information Processing Systems. Montreal/Canada, 2018.

Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time. (Long presentation)
Ghoshal A., Honorio J.
International Conference on Machine Learning. Stockholm/Sweden, 2018.

Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms.
Honorio J., Jaakkola T.
Uncertainty in Artificial Intelligence. New York, 2016.

2. Tensors and Social Networks

Exact Partitioning of High-order Planted Models with a Tensor Nuclear Norm Constraint. (Preprint)
Ke C., Honorio J.
(Under submission.)

Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation. (Preprint)
Ke C., Honorio J.
(Under submission.)

Information Theoretic Limits of Exact Recovery in Sub-hypergraph Models for Community Detection.
Liang J., Ke C., Honorio J.
IEEE International Symposium on Information Theory. Melbourne/Australia, 2021.

Information-Theoretic Limits for Community Detection in Network Models.
Ke C., Honorio J.
Neural Information Processing Systems. Montreal/Canada, 2018.

Information-Theoretic Lower Bounds for Recovery of Diffusion Network Structures.
Park K., Honorio J.
IEEE International Symposium on Information Theory. Barcelona/Spain, 2016.

3. Fairness

Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem. (Spotlight)
Barik A., Honorio J.
Neural Information Processing Systems. Virtual, 2021.

Fairness Constraints can Help Exact Inference in Structured Prediction.
Bello K., Honorio J.
Neural Information Processing Systems. Vancouver/Canada, 2020.

4. Federated Learning and Meta Learning

Federated Myopic Community Detection with One-shot Communication. (Preprint)
Ke C., Honorio J.
(Under submission.)

Exact Support Recovery in Federated Regression with One-shot Communication. (Preprint)
Barik A., Honorio J.
(Under submission.)

Meta Learning for Support Recovery in High-dimensional Precision Matrix Estimation.
Zhang Q., Zheng Y., Honorio J.
International Conference on Machine Learning. Virtual, 2021.

The Sample Complexity of Meta Sparse Regression.
Wang Z., Honorio J.
Artificial Intelligence and Statistics. California, 2021.

5. Reinforcement Learning and Deep Learning

A Simple Unified Framework for High Dimensional Bandit Problems. (Preprint)
Li W., Barik A., Honorio J.
(Under submission.)

Inverse Reinforcement Learning in the Continuous Setting with Formal Guarantees.
Dexter G., Bello K., Honorio J.
Neural Information Processing Systems. Virtual, 2021.

Information Theoretic Sample Complexity Lower Bound for Feed-Forward Fully-Connected Deep Networks. (Preprint)
Yang X., Honorio J.
(Under submission.)

A Lower Bound for the Sample Complexity of Inverse Reinforcement Learning.
Komanduru A., Honorio J.
International Conference on Machine Learning. Virtual, 2021.

On the Correctness and Sample Complexity of Inverse Reinforcement Learning.
Komanduru A., Honorio J.
Neural Information Processing Systems. Vancouver/Canada, 2019.

6. Game Theory

Provable Sample Complexity Guarantees for Learning of Continuous-Action Graphical Games with Nonparametric Utilities. (Preprint)
Barik A., Honorio J.
(Under submission.)

Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games. (Preprint)
Barik A., Honorio J.
(Under submission.)

Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity.
Ghoshal A., Honorio J.
Artificial Intelligence and Statistics. Canary Islands/Spain, 2018.

On the Sample Complexity of Learning Graphical Games.
Honorio J.
IEEE Allerton Conference on Communication, Control and Computing. Illinois, 2017.

Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions.
Ghoshal A., Honorio J.
Artificial Intelligence and Statistics. Florida, 2017.

From Behavior to Sparse Graphical Games: Efficient Recovery of Equilibria.
Ghoshal A., Honorio J.
IEEE Allerton Conference on Communication, Control and Computing. Illinois, 2016.

Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data.
Honorio J., Ortiz L.
Journal of Machine Learning Research, 16(Jun): pp. 1157-1210, 2015. [code]

7. Bayesian Networks

Direct Estimation of Difference Between Structural Equation Models in High Dimensions. (Preprint)
Ghoshal A., Honorio J.
(Under submission.)

Provable Efficient Skeleton Learning of Encodable Discrete Bayes Nets in Poly-Time and Sample Complexity.
Barik A., Honorio J.
IEEE International Symposium on Information Theory. California, 2020.

Learning Bayesian Networks with Low Rank Conditional Probability Tables.
Barik A., Honorio J.
Neural Information Processing Systems. Vancouver/Canada, 2019.

Learning Causal Bayes Networks Using Interventional Path Queries in Polynomial Time and Sample Complexity.
Bello K., Honorio J.
Neural Information Processing Systems. Montreal/Canada, 2018.

Learning Linear Structural Equation Models in Polynomial Time and Sample Complexity.
Ghoshal A., Honorio J.
Artificial Intelligence and Statistics. Canary Islands/Spain, 2018.

Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity.
Ghoshal A., Honorio J.
Neural Information Processing Systems. California, 2017.

Information-Theoretic Limits of Bayesian Network Structure Learning.
Ghoshal A., Honorio J.
Artificial Intelligence and Statistics. Florida, 2017.

8. Combinatorial Optimization and Continuous Optimization

First Order Methods take Exponential Time to Converge to Global Minimizers of Non-Convex Functions.
Kesari K., Honorio J.
IEEE International Symposium on Information Theory. Melbourne/Australia, 2021.

Reconstructing a Bounded-Degree Directed Tree Using Path Queries.
Wang Z., Honorio J.
IEEE Allerton Conference on Communication, Control and Computing. Illinois, 2019.

Convergence Rates of Biased Stochastic Optimization for Learning Sparse Ising Models.
Honorio J.
International Conference on Machine Learning. Edinburgh/Scotland, 2012. [code]

9. Markov Random Fields

On the Statistical Efficiency of L1,p Multi-Task Learning of Gaussian Graphical Models.
Honorio J., Jaakkola T., Samaras D.
Technical report, 2015. [code]

Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models.
Honorio J., Jaakkola T.
Uncertainty in Artificial Intelligence. Washington, 2013. [code]

Variable Selection for Gaussian Graphical Models.
Honorio J., Samaras D., Rish I., Cecchi G.
Artificial Intelligence and Statistics. Canary Islands/Spain, 2012. [code]

Lipschitz Parametrization of Probabilistic Graphical Models.
Honorio J.
Uncertainty in Artificial Intelligence. Barcelona/Spain, 2011.

Multi-Task Learning of Gaussian Graphical Models.
Honorio J., Samaras D.
International Conference on Machine Learning. Haifa/Israel, 2010. [code]

Sparse and Locally Constant Gaussian Graphical Models.
Honorio J., Ortiz L., Samaras D., Paragios N., Goldstein R.
Neural Information Processing Systems. Vancouver/Canada, 2009. [code]

10. Other Machine Learning Problems

Information Theoretic Limits for Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity. (Preprint)
Barik A., Honorio J.
(Under submission.)

A Le Cam Type Bound for Adversarial Learning and Applications.
Bello K., Xu Q., Honorio J.
IEEE International Symposium on Information Theory. Melbourne/Australia, 2021.

Information-Theoretic Bounds for Integral Estimation.
Adams D., Barik A., Honorio J.
IEEE International Symposium on Information Theory. Melbourne/Australia, 2021.

Information-Theoretic Lower Bounds for Zero-Order Stochastic Gradient Estimation.
Alabdulkareem A., Honorio J.
IEEE International Symposium on Information Theory. Melbourne/Australia, 2021.

Regularized Loss Minimizers with Local Data Perturbation: Consistency and Data Irrecoverability.
Li Z., Honorio J.
IEEE International Symposium on Information Theory. Melbourne/Australia, 2021.

Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation.
Ohnishi Y., Honorio J.
Artificial Intelligence and Statistics. California, 2021.

Optimality Implies Kernel Sum Classifiers are Statistically Efficient.
Meyer R., Honorio J.
International Conference on Machine Learning. California, 2019.

Cost-Aware Learning for Improved Identifiability with Multiple Experiments.
Guo L., Honorio J., Morgan J.
IEEE International Symposium on Information Theory. Paris/France, 2019.

Statistically and Computationally Efficient Variance Estimator for Kernel Ridge Regression.
Liu M., Honorio J., Cheng G.
IEEE Allerton Conference on Communication, Control and Computing. Illinois, 2018.

On the Statistical Efficiency of Compositional Nonparametric Prediction.
Xu Y., Honorio J., Wang X.
Artificial Intelligence and Statistics. Canary Islands/Spain, 2018.

The Error Probability of Random Fourier Features is Dimensionality Independent. (Preprint)
Li Y., Honorio J.
Technical report, 2018.

Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity.
Barik A., Honorio J., Tawarmalani M.
IEEE International Symposium on Information Theory. Aachen/Germany, 2017.

A Unified Framework for Consistency of Regularized Loss Minimizers.
Honorio J., Jaakkola T.
International Conference on Machine Learning. Beijing/China, 2014.

Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees.
Honorio J., Jaakkola T.
Artificial Intelligence and Statistics. Reykjavik/Iceland, 2014.

Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy.
Honorio J., Jaakkola T.
International Conference on Machine Learning. Atlanta, 2013.

11. Non-theoretical Work

Randomized Deep Structured Prediction for Discourse-Level Processing.
Widmoser M., Pacheco M., Honorio J., Goldwasser D.
European Association for Computational Linguistics Conference. Kyiv/Ukraine, 2021.

PrivSyn: Differentially Private Data Synthesis.
Zhang Z., Wang T., Honorio J., Li N., Backes M., He S., Chen J. Zhang Y.
USENIX Security Symposium. Vancouver/Canada, 2021.

Variable Selection in Gaussian Markov Random Fields.
Honorio J., Samaras D., Rish I., Cecchi G.
Invited book chapter in Log-Linear Models, Extensions and Applications.
Edited by Aravkin A., Deng L., Heigold G., Jebara T., Kanevski D., Wright S. (to be published on December, 2016.)

Predictive Sparse Modeling of fMRI Data for Improved Classification, Regression, and Visualization Using the k-Support Norm.
Belilovsky E., Gkirtzou K., Misyrlis M., Konova A., Honorio J., Alia-Klein N., Goldstein R., Samaras D., Blaschko M.
Computerized Medical Imaging and Graphics, 46(1): pp. 40-46, 2015.

Integration of PCA with a Novel Machine Learning Method for Reparameterization and Assisted History Matching Geologically Complex Reservoirs.
Honorio J., Chen C., Gao G., Du K., Jaakkola T.
Society of Petroleum Engineers: 91th Annual Technical Conference and Exhibition. Houston, 2015.

Classification on Brain Functional Magnetic Resonance Imaging: Dimensionality, Sample Size, Subject Variability and Noise.
Honorio J.
Invited book chapter in Frontiers of Medical Imaging.
Edited by Chen C., World Scientific Publishing Company, 2014.

Improving Interpretability of Graphical Models in fMRI Analysis via Variable-Selection.
Honorio J., Samaras D., Rish I., Cecchi G.
Organization for Human Brain Mapping, Anual Meeting. Hamburg/Germany, 2014.

Predicting Cross-task Behavioral Variables from fMRI Data Using the k-Support Norm. (Best paper award)
Misyrlis M., Konova A., Blaschko M., Honorio J., Alia-Klein N., Goldstein R., Samaras D.
Medical Image Computing and Computer-Assisted Intervention. Workshop on Sparsity Techniques in Medical Imaging. Boston, 2014.

Methylphenidate Enhances Executive Function and Optimizes Prefrontal Function in Both Health and Cocaine Addiction.
Moeller S., Honorio J., Tomasi D., Parvaz M., Woicik P., Volkow N., Goldstein R.
Cerebral Cortex, 24(3): pp. 643-653, 2014.

Integration of Principal Component Analysis and Streamline Information for the History Matching of Channelized Reservoirs.
Chen C., Gao G., Honorio J., Gelderblom P., Jimenez E., Jaakkola T.
Society of Petroleum Engineers: 90th Annual Technical Conference and Exhibition. Amsterdam/The Netherlands, 2014.

fMRI Analysis of Cocaine Addiction Using k-Support Sparsity.
Gkirtzou K., Honorio J., Samaras D., Goldstein R., Blaschko M.
IEEE International Symposium on Biomedical Imaging. California, 2013. [code]

fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics.
Gkirtzou K., Honorio J., Samaras D., Goldstein R., Blaschko M.
Medical Image Computing and Computer-Assisted Intervention, Workshop on Machine Learning in Medical Imaging. Nagoya/Japan, 2013. [code]

Can a Single Brain Region Predict a Disorder?
Honorio J., Tomasi D., Goldstein R., Leung H.C., Samaras D.
IEEE Transactions on Medical Imaging, 31(11): pp. 2062-2072, 2012. [code]

Dopaminergic Involvement During Mental Fatigue in Health and Cocaine Addiction.
Moeller S., Tomasi D., Honorio J., Volkow N., Goldstein R.
Translational Psychiatry, 2: e176, 2012.

Enhanced Midbrain Response at 6-month Follow-up in Cocaine Addiction, Association with Reduced Drug-related Choice.
Moeller S., Tomasi D., Woicik P., Maloney T., Alia-Klein N., Honorio J., Telang F., Wang G., Wang R., Sinha R., Carise D., Astone-Twerell J., Bolger J., Volkow N., Goldstein R.
Addiction Biology, 17(6): pp. 1013-25, 2012.

Two-person Interaction Detection Using Body-Pose Features and Multiple Instance Learning.
Yun K., Honorio J., Chattopadhyay D., Berg T., Samaras D.
IEEE Computer Vision and Pattern Recognition, Workshop on Human Activity Understanding from 3D Data. Rhode Island, 2012. [data]

Dopaminergic contribution to endogenous motivation during cognitive control breakdown.
Moeller S., Tomasi D., Honorio J., Volkow N., Goldstein R.
Society for Neuroscience. Washington DC, 2011.

Digital Analysis and Visualization of Swimming Motion.
Kirmizibayrak C., Honorio J., Jiang X., Mark R., Hahn J.
The International Journal of Virtual Reality, 10(3): pp. 9-16, 2011.

Digital Analysis and Visualization of Swimming Motion.
Kirmizibayrak C., Honorio J., Jiang X., Mark R., Hahn J.
Conference on Computer Animation and Social Agents, Simulation of Sports Motion Workshop. Chengdu/China, 2011.

Simple Fully Automated Group Classification on Brain fMRI.
Honorio J., Samaras D., Tomasi D., Goldstein R.
IEEE International Symposium on Biomedical Imaging. Rotterdam/The Netherlands, 2010. [code]

Disrupted Functional Connectivity with Dopaminergic Midbrain in Cocaine Abusers.
Tomasi D., Volkow N., Wang R., Honorio J., Maloney T., Alia-Klein N., Woicik P., Telang F., Goldstein R.
Public Library of Science, PLoS ONE, 5(5): e10815, 2010.

Oral Methylphenidate Normalizes Cingulate Activity in Cocaine Addiction During a Salient Cognitive Task.
Goldstein R., Woicik P., Maloney T., Tomasi D., Alia-Klein N., Shan J., Honorio J., Samaras D., Ruiliang W., Telang F., Wang G., Volkow N.
Proceedings of the National Academy of Sciences, 107(38): pp. 16667-72, 2010.

Learning Brain fMRI Structure Through Sparseness and Local Constancy.
Honorio J., Ortiz L., Samaras D., Goldstein R.
Neural Information Processing Systems, Workshop on Connectivity Inference in NeuroImaging. Vancouver/Canada, 2009.

A Functional Geometry of fMRI BOLD Signal Interactions.
Langs G., Samaras D., Paragios N., Honorio J., Golland P., Alia-Klein N., Tomasi D., Volkow N., Goldstein R.
Neural Information Processing Systems, Workshop on Connectivity Inference in NeuroImaging. Vancouver/Canada, 2009.

Dopaminergic Response to Drug Words in Cocaine Addiction.
Goldstein R., Tomasi D., Alia-Klein N., Honorio J., Maloney T., Woicik P., Wang R., Telang F., Volkow N.
Journal of Neuroscience, 29(18): pp. 6001-6, 2009.

Anterior Cingulate Cortex Hypoactivations to an Emotionally Salient Task in Cocaine Addiction.
Goldstein R., Alia-Klein N., Tomasi D., Honorio J., Maloney T., Woicik P., Wang R., Telang F., Volkow N.
Proceedings of the National Academy of Sciences, 106(23): pp. 9453-8, 2009.

Task-Specific Functional Brain Geometry from Model Maps.
Langs G., Samaras D., Paragios N., Honorio J., Alia-Klein N., Tomasi D., Volkow N., Goldstein R.
Medical Image Computing and Computer-Assisted Intervention. New York, 2008.